Object-Based Informal Settlement Mapping in Google Earth Engine Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Abstract

Mapping informal settlements’ diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity.

Monitoring land cover change and its estimated carbon loss in the Nusantara capital city Indonesia on high-resolution planet satellite imagery

“…SNIC represents a refinement of the Simple Linear Iterative Clustering (SLIC) approach, which stands as one of the widely employed superpixel clustering algorithms 42,43 . In contrast to alternative clustering methodologies like K-means and G-means 44,45 , the preference for the SNIC algorithm underscores our choice for conducting the clustering process within this study.…”

Using Local Knowledge and Remote Sensing in the Identification of Informal Settlements in Riyadh City, Saudi Arabia

“…However, a drawback of this timeframe is that due to the dynamic nature of these populations, the data are often out of date. An associated issue is that information related to slum location or concentration may also not be available, as data from these areas are often not collected for formal statistical studies [14,15].…”

“…A comparsion between the classification result and the sampled segments was then undertaken to define the classification accuracy. Using the methodology of Matarira et al [15], 1750 random points were selected as the reference data in the GeoEye-1 image. The results were used to compare OBIA classification using EK with the alternative OBIA classification which did not use EK-identified indicators.…”

The Expression of Illegal Urbanism in the Urban Morphology and Landscape: The Case of the Metropolitan Area of Seville (Spain)

“…Informal urban growth leaves easily traceable impressions in the territory and is fundamental for understanding the urban form of the main cities of the so-called global south [1]. An example of this can be found in the larger Latin American cities, where a very high percentage of their inhabitants live in settlements that have emerged outside regulated urban processes [2][3][4].…”

“…This is because sources such as Corine Land Cover (CLC) are often used, which, while allowing for a very accurate diachronic analysis of the growth process [11] or of the resulting urban models applied to medium-sized cities [12], do not allow for the distinction between planned and illegal or unplanned urban developments [13]. More recently, the possibilities offered by Google Earth in combination with satellite data for the characterisation and evolution of the particularities of informal settlements have been shown [1]. One of the consequences of using those sources is the impossibility of distinguishing the different consequences of those urban expansions being planned or those outside the urban planning law.…”